• The Transactions of the Korean Institute of Electrical Engineers P
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• v.53 no.4
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• pp.195-200
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• 2004

Many kinds of speed sensorless control system of induction motor had been developed. But it is difficult to implement at the real system because of complex algorithm and equations. This paper investigates a novel speed sensorless control of induction motor using neural networks. The proposed control strategy is based on neural networks using stator current and output of neural model based on state observer. The errors between the stator current and the output of neural model are back-propagated to adjust the rotor speed, so that adaptive state variable will coincide with the desired state variable. This algorithm may overcome several shortages of conventional model, such as integrator problems, small EMF at low speed and relatively large sensitivity of stator resistance variation. Also, this paper presents a newly developed optimal equation about the momentum constant and the learning rate. The proposed algorithms are verified through simulation.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05b
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• pp.241-244
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• 2006

The basic prediction model was constructed to obtain optimal maintenance method for concrete structure under marine environment by exploring the mechanism of mono and combined deterioration in lab. This model was planned to be upgraded with data acquired from several exposure specimens under same environment as structures. The computer program developed to give useful guidance observer would be improved. Several repair materials and repair construction methods applied to exposure specimens will be tested for its performance of prohibit salt attack and freezing & thawing action during experimental period about ten years. All of these data could be available to complete the prediction system. The manager will be able to use the system for optimal maintenance of marine concrete structures.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.11a
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• pp.531-534
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• 2005

The basic prediction model was constructed to obtain optimal maintenance method for concrete structure under marine environment by exploring the mechanism of mono and combined deterioration in lab. This model was planned to be upgraded with data acquired from several exposure specimens under same environment as structures. The computer program developed to give useful guidance observer would be improved. Several repair materials and repair construction methods applied to exposure specimens will be tested for its performance of prohibit salt attack and freezing & thawing action during experimental period about ten years. All of these data could be available to complete the prediction system. The manager will be able to use the system for optimal maintenance of marine concrete structures.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.579-582
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• 1999

This paper presents a design method of deadbeat controller for DC motor driving a rotational system with gear. The results of sampling a continuous-data process does not guarantee that no ripples occur between the sampling instants in the continuous-data output, but the proposed deadbeat control system that consists of the integral controller and the full state observer, and zero order hold using in continuous systems, has many advantages of such as an output response without the ripple and reaching the steady state without error after a given sampling period and faster settling time than the optimal control system in the short sampling period. The results of case study through matlab simulation shown that the efficiency of the proposed controller for DC motor driving a rotational system with gear, is verified by comparing with optimal controller etc.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.3
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• pp.31-42
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• 1985

Thig paper used two feedback sensors, that is, potentiometer and tachometer in order to control DC motor. Also, the state feedback and kalman regular type in the linear system or the state feedback and on-off relay type in the non-linear system are used as control meth-ods for optimal control values. This compared and analyzed the control estimate of tracking angles by the estimate of three branches of methods of position and speed measured, position and speed by PD and position, speed and covariance by an observer.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.420-422
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• 1998

In this paper, we propose a position-speed control of servo system with a state estimator. And also we utilized two mass modelling in order to deals with real system accurately. The overall control system consists of two parts: the position-speed controller and state estimator. The Kalman filter applied as state - feedback controller is an optimal state estimator applied to a dynamic system that involves random perturbations and gives a linear,unbiased and minimun error variance recursive algorithm to estimate the unknown state optimally. Therefore we consider the error problem about the servo system modelling, the measurement noise at low-speed ranges a stochastic system, and implement a optimal state observer. Performance of the proposed state estimator are demonstrated by computer simulations.

• Journal of Drive and Control
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• v.14 no.2
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• pp.16-22
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• 2017

This paper presents the algorithm of an adaptive model-free-control-based steering control for multi-axle all-terrain cranes for which the recursive least squares with forgetting are applied. To optimally control the actual system in the real world, the linear or nonlinear mathematical model of the system should be given for the determination of the optimal control inputs; however, it is difficult to derive the mathematical model due to the actual system's complexity and nonlinearity. To address this problem, the proposed adaptive model-free controller is used to control the steering angle of a multi-axle crane. The proposed model-free control algorithm uses only the input and output signals of the system to determine the optimal inputs. The recursive least-squares algorithm identifies first-order systems. The uncertainty between the identified system and the actual system was estimated based on the disturbance observer. The proposed control algorithm was used for the steering control of a multi-axle crane, where only the steering input and the desired yaw rate were employed, to track the reference path. The controller and performance evaluations were constructed and conducted in the Matlab/Simulink environment. The evaluation results show that the proposed adaptive model-free-control-based steering-control algorithm produces a sound path-tracking performance.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.1
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• pp.81-90
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• 2002

This paper presents an Implementation of digital high-performance position sensorless control system of Reluctance Synchronous Motor(RSM) drives with Direct Torque Control(DTC). The system consists of stator flux observer, speed and torque estimator, two digital hysteresis controllers, an optimal switching look-up table, Insulated Gate Bipolar Transistor(IGBT) voltage source inverter, and TMS320C31 DSP board. The stator flux observer Is based on the combined voltage and current model with stator flux feedback adaptive control of which inputs are current and voltage sensed on motor terminal for wide speed range. In order to prove the suggested sensorless control algorithm for industrial field application, we have some simulation and actual experiment at low and high speed range. The developed high-performance speed control by fully digital system are shown a good response characteristic of control results and high performance features using 1.0[kW] RSM having 2.57 reluctance ratio of $L_d/L_q$.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.2
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• pp.68-76
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• 2002

This paper presents an implementation of high-dynamic performance control system of Reluctance Synchronous Motor (RSM) drives for an industrial servo system with direct torque control (DTC). The problems of DTC for high-dynamic performance and maximum efficiency RSM drives are the nonlinear variable flux and inductance due to a saturated stator linkage flux and nonlinear inductance curve with various load currents. The accurate estimation of the stator flux and torque are obtained using stator flux observer of which a saturated inductance Ld and Lq can be compensated by using the adapted neural network from measuring the modulus and angle of the stator current. To obtain fast torque response and maximum torque/current with varying load current, the reference command flux is ensured by imposing Ids=Iqs. This control strategy is proposed to fast response and optimal efficiency for RSM drive. In order to prove rightness of the suggested control algorithm, we have some actual experimental system using 6000 pulse/rev encoder at ${\pm}10$ and ${\pm}1500rpm$. The developed digitally high-performance control system are shown some good response characteristics of control results and high performance features using 1.0kW RSM of which has 2.57 Ld/Lq salient ratio.

• Journal of Mechanical Science and Technology
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• v.20 no.11
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• pp.1898-1913
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• 2006

This paper deals with the design of a yaw rate controller based on gain-scheduled H$\infty$ optimal control, which is intended to maintain the lateral stability of a vehicle. Uncertain factors such as vehicle mass and cornering stiffness in the vehicle yaw rate dynamics naturally call for the robustness of the feedback controller and thus H$\infty$ optimization technique is applied to synthesize a controller with guaranteed robust stability and performance against the model uncertainty. In the implementation stage, the feed-forward yaw moment by driver's steer input is estimated by the disturbance observer in order to determine the accurate compensatory moment. Finally, HILS results indicate that the proposed yaw rate controller can satisfactorily improve the lateral stability of an automobile.